Fight. Heal. Repeat: A Look at Rhetorical Devices in Grinding Game Mechanics
Bibliographic record
Abstract
Background Common definitions of rhetoric in games such as Bogost’s ‘procedural rhetoric’ have their basis in the Aristotelian definition of rhetoric, which concerns itself with discovering all means of persuasion in language. Purpose Gaming rhetoric has more to do with inducing action in players, and therefore falls more in line with Kenneth Burke’s definition of rhetoric. Grinding is a gaming mechanic that can be analysed using rhetorical devices if Burke’s definition of rhetoric is held at the core of this understanding. This article posits that games that employ a particular game mechanic, that of ‘grinding’, are relying on a specific rhetorical device in their design known as ploke, which then persuades the player to continue to do an action multiple times over, and therefore persuade players to form attitudes that align with the designer’s rhetorical goals. Analysis An analysis of ploke was applied to three specific games: Runescape (2001), Ha des (2018) and Animal Crossing: New Horizons (2020). These games were chosen based on the ability to look at multiple genres as well as multiple different points in modern game development history. Ploke provided the ability to understand the method in which grinding communicates with players, enticing and incentivizing them to continue to complete actions repeatedly, whether for story progression or skill enhancement. The rhetorical power of ploke is found in its repetition, and since ploke describes the use of repetition in rhetorical contexts, thus grinding’s rhetorical power can be explained through this rhetorical phenomenon. Conclusion Ploke is just one rhetorical device, and grinding is just one game mechanic. There are several other game mechanics that can be analysed through rhetorical devices. This analysis allows researchers in interdisciplinary fields of games and linguistics, communication or humanities to explore how games communicate and influence player decisions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".